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python/mxnet/optimizer/adamW.py
233 строки
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Zheyu Ye
fix typo in adamw (#19680)
16 дек 2020, 09:59
Не верифицирован
16 дек 2020, 09:59
91503f7
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# coding: utf-8 # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """AdamW optimizer.""" import math import os import numpy as np from .optimizer import Optimizer, register from ..ndarray import (zeros, clip, sqrt, square, full, NDArray) from ..ndarray.contrib import mp_adamw_update, adamw_update,\ multi_mp_adamw_update, multi_adamw_update __all__ = ['AdamW'] @register class AdamW(Optimizer): """The AdamW optimizer. This class implements the optimizer described in *Decoupled Weight Decay Regularization*, available at https://arxiv.org/pdf/1711.05101.pdf. Updates are applied by:: grad = clip(grad * rescale_grad, clip_gradient) m = beta1 * m + (1 - beta1) * grad v = beta2 * v + (1 - beta2) * (grad**2) lr = learning_rate * sqrt(1 - beta2**t) / (1 - beta1**t) w = w - lr * (m / (sqrt(v) + epsilon) + wd * w) Also, we can turn off the bias correction term and the updates are as follows:: grad = clip(grad * rescale_grad, clip_gradient) + wd * weight m = beta1 * m + (1 - beta1) * grad v = beta2 * v + (1 - beta2) * (grad**2) lr = learning_rate w = w - lr * (m / (sqrt(v) + epsilon) + wd * w) This optimizer accepts the following parameters in addition to those accepted by :class:`.Optimizer`. Parameters ---------- learning_rate : float, default 0.001 The initial learning rate. If None, the optimization will use the learning rate from ``lr_scheduler``. If not None, it will overwrite the learning rate in ``lr_scheduler``. If None and ``lr_scheduler`` is also None, then it will be set to 0.01 by default. beta1 : float, default 0.9 Exponential decay rate for the first moment estimates. beta2 : float, default 0.999 Exponential decay rate for the second moment estimates. epsilon : float, default 1e-6 Small value to avoid division by 0. correct_bias : bool, default True Can be set to False to avoid correcting bias in Adam (e.g. like in Bert TF repository). Default True. use_fused_step : bool, default True Whether or not to use fused kernels for optimizer. When use_fused_step=False, step is called, otherwise, fused_step is called. """ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-6, correct_bias=True, use_fused_step=True, **kwargs): super().__init__(use_fused_step=use_fused_step, learning_rate=learning_rate, **kwargs) self.beta1 = beta1 self.beta2 = beta2 self.epsilon = epsilon self.correct_bias = correct_bias self.aggregate_num = max(1, min(50, int(os.getenv('MXNET_OPTIMIZER_AGGREGATION_SIZE', '4')))) def create_state(self, index, weight): """state creation function.""" return (zeros(weight.shape, weight.context, dtype=weight.dtype), # mean zeros(weight.shape, weight.context, dtype=weight.dtype)) # variance def step(self, indices, weights, grads, states): """Perform an optimization step using gradients and states. Parameters ---------- indices : list of int List of unique indices of the parameters into the individual learning rates and weight decays. Learning rates and weight decay may be set via `set_lr_mult()` and `set_wd_mult()`, respectively. weights : list of NDArray List of parameters to be updated. grads : list of NDArray List of gradients of the objective with respect to this parameter. states : List of any obj List of state returned by `create_state()`. """ for index, weight, grad, state in zip(indices, weights, grads, states): self._update_count(index) lr = self._get_lr(index) wd = self._get_wd(index) t = self._index_update_count[index] # preprocess grad grad *= self.rescale_grad if self.clip_gradient is not None: grad = clip(grad, - self.clip_gradient, self.clip_gradient) if self.correct_bias: coef1 = 1. - self.beta1**t coef2 = 1. - self.beta2**t lr *= math.sqrt(coef2) / coef1 # update mean and var mean, var = state mean[:] *= self.beta1 mean[:] += (1. - self.beta1) * grad var[:] *= self.beta2 var[:] += (1. - self.beta2) * square(grad) # update weight d = mean / (sqrt(var) + self.epsilon) weight[:] -= lr * d # add wd if wd > 0: weight[:] -= lr * wd * weight def fused_step(self, indices, weights, grads, states): """Perform a fused optimization step using gradients and states. Fused kernel is used for update. Parameters ---------- indices : list of int List of unique indices of the parameters into the individual learning rates and weight decays. Learning rates and weight decay may be set via `set_lr_mult()` and `set_wd_mult()`, respectively. weights : list of NDArray List of parameters to be updated. grads : list of NDArray List of gradients of the objective with respect to this parameter. states : List of any obj List of state returned by `create_state()`. """ multi_precision = self.multi_precision and weights[0].dtype == np.float16 aggregate = self.aggregate_num > 1 if not isinstance(indices, (tuple, list)): indices = [indices] weights = [weights] grads = [grads] states = [states] for w_i, g_i in zip(weights, grads): assert(isinstance(w_i, NDArray)) assert(isinstance(g_i, NDArray)) aggregate = (aggregate and w_i.stype == 'default' and g_i.stype == 'default') self._update_count(indices) lrs = self._get_lrs(indices) wds = self._get_wds(indices) if self.correct_bias: new_lrs = [] for idx, lr in zip(indices, lrs): t = self._index_update_count[idx] coef1 = 1. - self.beta1 ** t coef2 = 1. - self.beta2 ** t new_lrs.append(lr * math.sqrt(coef2) / coef1) lrs = new_lrs if not isinstance(self.rescale_grad, NDArray): self.rescale_grad = full(shape=(1,), val=self.rescale_grad, ctx=weights[0].context) else: self.rescale_grad = self.rescale_grad.as_in_context(weights[0].context) kwargs = {'beta1': self.beta1, 'beta2': self.beta2, 'epsilon': self.epsilon, 'rescale_grad': self.rescale_grad} if self.clip_gradient: kwargs['clip_gradient'] = self.clip_gradient if aggregate: current_index = 0 while current_index < len(indices): sidx = current_index eidx = min(current_index + self.aggregate_num, len(indices)) if not multi_precision: mean, var = list(zip(*states[sidx:eidx])) multi_adamw_update(weights[sidx:eidx], grads[sidx:eidx], mean, var, out=weights[sidx:eidx], size=len(weights[sidx:eidx]), lrs=list(np.ones(len(weights[sidx:eidx]))), wds=wds[sidx:eidx], etas=lrs[sidx:eidx], **kwargs) else: mean_var = list(zip(*states[sidx:eidx]))[0] tmean_var = list(zip(*mean_var)) mean = tmean_var[0] var = tmean_var[1] multi_mp_adamw_update(weights[sidx:eidx], grads[sidx:eidx], mean, var, list(zip(*states[sidx:eidx]))[1], out=weights[sidx:eidx], size=len(weights[sidx:eidx]), lrs=list(np.ones(len(weights[sidx:eidx]))), wds=wds[sidx:eidx], etas=lrs[sidx:eidx], **kwargs) current_index += self.aggregate_num else: for w_i, g_i, s_i, lr, wd in zip(weights, grads, states, lrs, wds): if not multi_precision: mean, var = s_i adamw_update(w_i, g_i, mean, var, out=w_i, lr=1, wd=wd, eta=lr, **kwargs) else: mean, var = s_i[0] mp_adamw_update(w_i, g_i, mean, var, s_i[1], out=w_i, lr=1, wd=wd, eta=lr, **kwargs)